ProteinGAN: A generative adversarial network that generates functional protein sequences

Proteins are large, highly complex and naturally occurring molecules can be found in all living organisms. These unique substances, which consist of amino acids joined together by peptide bonds to form long chains, can have a variety of functions and properties. The specific order in which different amino acids are arranged to form a given protein ultimately determines the protein's 3D structure, physicochemical properties and molecular function. While scientists have been studying proteins for decades, designing proteins that elicit specific chemical reactions has so far proved to be highly challenging. Researchers at Biomatter Designs, Vilnius University in Lithuania, and Chalmers University of Technology in Sweden have recently developed ProteinGAN, a generative adversarial network (GAN) that can process and'learn' different natural protein sequences.